Abstract
To improve our understanding of multi-drug therapies, cancer cell line panels screened with drug combinations are frequently studied using machine learning (ML). ML models trained on such data typically focus on predicting synergy scores that support drug development and repurposing efforts but have limitations when deriving personalized treatment recommendations. To simulate a more realistic personalized treatment scenario, we pioneer ML models that make dose-specific predictions of the relative growth inhibition (instead of synergy scores), and that can be applied to previously unseen cell lines. Our approach is highly flexible: it enables the reconstruction of dose-response curves and matrices, as well as various measures of drug sensitivity (and synergy) from model predictions, which can finally even be used to derive cell line-specific prioritizations of both mono- and combination therapies.